Function-Based Troposphere Tomography Technique for Optimal Downscaling of Precipitation

Precipitation is an important meteorological indicator that has a direct and significant impact on ecology, agriculture, hydrology, and other vital areas of human health and life. It is therefore essential to monitor variations of this parameter at a global and local scale. To monitor and predict long-term changes in climate elements, Global Circulation Models (GCMs) can provide simulated global-scale climatic processes. Due to the low spatial resolution of these models, downscaling methods are required to convert such large-scale information to regional-scale data for local applications. Among the downscaling methods, the Statistical DownScaling Model (SDSM) and the Artificial Neural Networks (ANNs) are widely used due to their low computational volume and suitable output. These models mainly require training data, and generally, the reanalysis data obtained from the National Center for Environmental Prediction (NCEP) and European Centre for Medium-range Weather Forecasts (ECMWF) are used for this purpose. With an optimal downscaling method, instead of applying the humidity indices extracted from ECMWF data, the outputs of the function-based tropospheric tomography technique obtained from the Global Navigation Satellite System (GNSS) will be used. The reconstructed function-based tropospheric data is then fed to the SDSM and ANN methods used for downscaling. The results of both methods indicate that the tomography can increase the accuracy of the downscaling process by about 20 mm in the wet months of the year. This corresponds to an average improvement of 38% with regard to the root mean square error (RMSE) of the monthly precipitation.

Epidemiological Characteristics of Parainfluenza Virus Type 3 and the Effects of Meteorological Factors in Hospitalized Children With Lower Respiratory Tract Infection

OBJECTIVE

To investigate the relationship between meteorological factors and Human parainfluenza virus type 3 (HPIV-3) infection among hospitalized children.

METHODS

All hospitalized children with acute lower respiratory tract infections were tested for viral pathogens and enrolled, at the second affiliated hospital of Wenzhou medical university, between 2008 and 2017. Meteorological data were directly obtained from Wenzhou Meteorology Bureau's nine weather stations and expressed as the mean exposure for each 10-day segment (average daily temperatures, average daily relative humidity, rainfall, rainfall days, and wind speed). The correlation between meteorological factors and the incidence of HPIV-3 was analyzed, with an autoregressive integrated moving average model (ARIMA), generalized additive model (GAM), and least absolute shrinkage and selection operator (LASSO).

RESULTS

A total of 89,898 respiratory specimens were tested with rapid antigen tests, and HPIV-3 was detected in 3,619 children. HPIV-3 was detected year-round, but peak activities occurred most frequently from March to August. The GAM and LASSO-based model had revealed that HPIV-3 activity correlated positively with temperature and rainfall day, but negatively with wind speed. The ARIMA (1,0,0)(0,1,1) model well-matched the observed data, with a steady R² reaching 0.708 (Ljung-Box Q = 21.178, P = 0.172).

CONCLUSION

Our study suggests that temperature, rainfall days, and wind speed have significant impacts on the activity of HPIV-3. GAM, ARIMA, and LASSO-based models can well predict the seasonality of HPIV-3 infection among hospitalized children. Further understanding of its mechanism would help facilitate the monitoring and early warning of HPIV-3 infection.

Applying elastic-net regression to identify the best models predicting changes in civic purpose during the emerging adulthood

INTRODUCTION

Changes in civic purpose during the emerging adulthood has been a significant research topic since it is closely associated with active civic engagement later in human lives. While standard regression methods have been used in previous studies to predict civic purpose development, they have limitations that may not always lead to best prediction models. We aimed to address these limitations by utilizing elastic-net multinomial logistic regression, which favors models with the least number of necessary predictors, in exploration of predictors for civic purpose development in a data-driven manner.

METHODS

We analyzed data from the longitudinal Civic Purpose Project while focusing on the model that best predicted civic purpose from Wave 1 (12th grade before high school graduation) to Wave 2 (two years after Wave 1). The reanalyzed data included responses from 476 participants (60.29% females, 39.08% males) who were recruited from Californian high schools in the United States and completed the survey at both Waves. The elastic-net regression was performed 5000 times for predicting three dependent variables, Wave 2 political purpose, community service purpose, and expressive activity purpose, with Wave 1 predictors. We identified which predictors were selected as the constituents of the best regression models during the elastic-net regression process.

RESULTS

Results showed that civic purpose, moral and political identity, and external supports (e.g., parental and peer involvement, school civic opportunities, etc.) in Wave 1 significantly predicted civic purpose in Wave 2. Several predictors were excluded from the regression models during the elastic-net regression process.

CONCLUSION

We found that the elastic-net regression was able to present the more regularized model for prediction. Implications for promoting civic purpose are discussed as well as utilizing the elastic-net regression method.

Quantifying climate, streamflow, and watershed control on water quality across Southeastern US watersheds

Identification of the key variables that influence spatial variation in stream water quality is crucial for designing sustainable water management strategies. In this study, we investigated the key variables that influence the spatial variability of stream of water quality, across multiple watersheds. This study uses water quality data collected over 19 years for 59 watersheds located in the Southeast Atlantic region of the United States, which includes the states of North Carolina, South Carolina, and Georgia. A conceptual modeling framework was developed to understand the linkage between the long-term mean water quality constituents (Total nitrogen, Total phosphorus, Turbidity, and pH) and the watershed characteristics (e.g., topography, land use/cover, soil type), streamflow data, and climatic variables (precipitation and temperature). The modeling results suggest that not only anthropogenic variables influence the mean water quality constituents, but other watershed characteristics, such as soil properties, have a significant impact. The natural watershed characteristics explain most of the spatial variability in the mean Turbidity and pH values in streams. The modeling results also suggest that once land use and soil properties are considered, watershed topography has a limited role to explain the variation in the mean water quality. Overall, the developed watershed models can be used to forecast stream water-quality responses to future land use, climate, soil, and land management changes within the study area.

Meteorological factors affecting respiratory syncytial virus infection: A time-series analysis

INTRODUCTION

Respiratory syncytial virus (RSV) infection is a major cause of hospitalization in children. Meteorological factors are known to influence seasonal RSV epidemics, but the relationship between meteorological factors and RSV infection in children is not well understood. We aimed to explore the relationship between meteorological factors and RSV infections among hospitalized children, using different statistical models.

METHODS

We conducted a retrospective review concerning children with RSV infections admitted to a tertiary pediatric hospital in Wenzhou, China, between January 2008 and December 2017. The relationship between meteorological factors (average daily temperatures, average daily relative humidity, rainfall, rainfall days, and wind speed) and the incidence of RSV in hospitalized children was analyzed using three time-series models, namely an autoregressive integrated moving average (ARIMA) model, a generalized additive model (GAM), and a least absolute shrinkage and selection operator (LASSO)-based model.

RESULTS

In total, 15 858 (17.6%) children tested positive for RSV infection. The ARIMA model revealed a marked seasonal pattern in the RSV detection rate, which peaked in winter and spring. The model was a good predictor of RSV incidence (R² : 83.5%). The GAM revealed that a lower temperature and higher wind speed preceded increases in RSV detection. The LASSO-based model revealed that temperature and relative humidity were negatively correlated with RSV detection.

CONCLUSIONS

Seasonality of RSV infection in hospitalized children correlated strongly with temperature. The LASSO-based model can be used to predict annual RSV epidemics using weather forecast data.

Selection of CMIP5 GCM Ensemble for the Projection of Spatio-Temporal Changes in Precipitation and Temperature over the Niger Delta, Nigeria

Selection of a suitable general circulation model (GCM) ensemble is crucial for effective water resource management and reliable climate studies in developing countries with constraint in human and computational resources. A careful selection of a GCM subset by excluding those with limited similarity to the observed climate from the existing pool of GCMs developed by different modeling centers at various resolutions can ease the task and minimize uncertainties. In this study, a feature selection method known as symmetrical uncertainty (SU) was employed to assess the performance of 26 Coupled Model Intercomparison Project Phase 5 (CMIP5) GCM outputs under Representative Concentration Pathway (RCP) 4.5 and 8.5. The selection was made according to their capability to simulate observed daily precipitation (prcp), maximum and minimum temperature (Tmax and Tmin) over the historical period 1980–2005 in the Niger Delta region, which is highly vulnerable to extreme climate events. The ensemble of the four top-ranked GCMs, namely ACCESS1.3, MIROC-ESM, MIROC-ESM-CHM, and NorESM1-M, were selected for the spatio-temporal projection of prcp, Tmax, and Tmin over the study area. Results from the chosen ensemble predicted an increase in the mean annual prcp between the range of 0.26% to 3.57% under RCP4.5, and 0.7% to 4.94% under RCP 8.5 by the end of the century when compared to the base period. The study also revealed an increase in Tmax in the range of 0 to 0.4 °C under RCP4.5 and 1.25–1.79 °C under RCP8.5 during the periods 2070–2099. Tmin also revealed a significant increase of 0 to 0.52 °C under RCP4.5 and between 1.38–2.02 °C under RCP8.5, which shows that extreme events might threaten the Niger Delta due to climate change. Water resource managers in the region can use these findings for effective water resource planning, management, and adaptation measures.

EMD-regression for modelling multi-scale relationships, and application to weather-related cardiovascular mortality

In a number of environmental studies, relationships between nat4ural processes are often assessed through regression analyses, using time series data. Such data are often multi-scale and non-stationary, leading to a poor accuracy of the resulting regression models and therefore to results with moderate reliability. To deal with this issue, the present paper introduces the EMD-regression methodology consisting in applying the empirical mode decomposition (EMD) algorithm on data series and then using the resulting components in regression models. The proposed methodology presents a number of advantages. First, it accounts of the issues of non-stationarity associated to the data series. Second, this approach acts as a scan for the relationship between a response variable and the predictors at different time scales, providing new insights about this relationship. To illustrate the proposed methodology it is applied to study the relationship between weather and cardiovascular mortality in Montreal, Canada. The results shed new knowledge concerning the studied relationship. For instance, they show that the humidity can cause excess mortality at the monthly time scale, which is a scale not visible in classical models. A comparison is also conducted with state of the art methods which are the generalized additive models and distributed lag models, both widely used in weather-related health studies. The comparison shows that EMD-regression achieves better prediction performances and provides more details than classical models concerning the relationship.

Application of Penalized Regression Techniques in Modelling Insulin Sensitivity by Correlated Metabolic Parameters

This paper aims to introduce penalized estimation techniques in clinical investigations of diabetes, as well as to assess their possible advantages and limitations. Data from a previous study was used to carry out the simulations to assess: a) which procedure results in the lowest prediction error of the final model in the setting of a large number of predictor variables with high multicollinearity (of importance if insulin sensitivity should be predicted) and b) which procedure achieves the most accurate estimate of regression coefficients in the setting of fewer predictors with small unidirectional effects and moderate correlation between explanatory variables (of importance if the specific relation between an independent variable and insulin sensitivity should be examined). Moreover a special focus is on the correct direction of estimated parameter effects, a non-negligible source of error and misinterpretation of study results. The simulations were performed for varying sample size to evaluate the performance of LASSO, Ridge as well as different algorithms for Elastic Net. These methods were also compared with automatic variable selection procedures (i.e. optimizing AIC or BIC).We were not able to identify one method achieving superior performance in all situations. However, the improved accuracy of estimated effects underlines the importance of using penalized regression techniques in our example (e.g. if a researcher aims to compare relations of several correlated parameters with insulin sensitivity). However, the decision which procedure should be used depends on the specific context of a study (accuracy versus complexity) and moreover should involve clinical prior knowledge.

A tutorial on variable selection for clinical prediction models: feature selection methods in data mining could improve the results

OBJECTIVES

Identifying an appropriate set of predictors for the outcome of interest is a major challenge in clinical prediction research. The aim of this study was to show the application of some variable selection methods, usually used in data mining, for an epidemiological study. We introduce here a systematic approach.

STUDY DESIGN AND SETTING

The P-value-based method, usually used in epidemiological studies, and several filter and wrapper methods were implemented to select the predictors of diabetes among 55 variables in 803 prediabetic females, aged ≥ 20 years, followed for 10-12 years. To develop a logistic model, variables were selected from a train data set and evaluated on the test data set. The measures of Akaike information criterion (AIC) and area under the curve (AUC) were used as performance criteria. We also implemented a full model with all 55 variables.

RESULTS

We found that the worst and the best models were the full model and models based on the wrappers, respectively. Among filter methods, symmetrical uncertainty gave both the best AUC and AIC.

CONCLUSION

Our experiment showed that the variable selection methods used in data mining could improve the performance of clinical prediction models. An R program was developed to make these methods more feasible and visualize the results.